Cross-validation ensures the model will always overfit by testing on multiple subsets of data, thereby guaranteeing high accuracy on the training data.

By systematically varying the training dataset size, cross-validation directly minimizes computational costs without affecting model performance.

Cross-validation evaluates the model's generalization to unseen data while maintaining data privacy, by using distinct subsets for training and validation.

The technique prevents the model from learning any meaningful patterns by frequently altering the training dataset, ensuring compliance with privacy regulations.

Through performance evaluation across different data splits, cross-validation aids in selecting model parameters that achieve optimal accuracy and comply with data privacy laws.